| # RUN: %PYTHON %s | FileCheck %s |
| |
| import torch |
| import numpy as np |
| |
| from mpact.mpactbackend import mpact_jit, mpact_jit_compile, mpact_jit_run |
| |
| from mpact.models.resnet import resnet20 |
| |
| resnet = resnet20() |
| resnet.train(False) # switch to inference |
| |
| # Get a random input. |
| # B x RGB x H x W |
| x = torch.rand(1, 3, 16, 16) |
| |
| # |
| # CHECK: pytorch |
| # CHECK: mpact |
| # CHECK: passed |
| # |
| |
| with torch.no_grad(): |
| # Run it with PyTorch. |
| print("pytorch") |
| res1 = resnet(x) |
| print(res1) |
| |
| # Run it with MPACT. |
| print("mpact") |
| res2 = mpact_jit(resnet, x) |
| print(res2) |
| |
| # Completely different inputs and weights for each run, |
| # so we simply verify the two results are the same. |
| np.testing.assert_allclose(res1.numpy(), res2, rtol=1e-5, atol=0) |
| print("passed") |
